def main():
if len(sys.argv) != 6:
print 'Usage: <goods.simi> <goods.docinfo> <goods.name> <name.me> <goods.simi.output>'
sys.exit(0)
print 'docinfo...'; sys.stdout.flush()
gidx2id = read_docinfo(sys.argv[2])
print 'simi...'; sys.stdout.flush()
gid2simi = read_simi(sys.argv[1], gidx2id)
print 'name...'; sys.stdout.flush()
gid2name = read_name(sys.argv[3])
print 'name.me...'; sys.stdout.flush()
name_me = read_me(sys.argv[4])
kvg = KVEngine()
print 'binfo...'; sys.stdout.flush()
kvg.load([full_path('goods_binfo.kv')])
kvg.load([full_path('goods_price.kv')])
print 'shop limit...'; sys.stdout.flush()
same_shop_limit(gid2simi, kvg)
print 'mutually_exclusive_names...'; sys.stdout.flush()
mutually_exclusive_names(gid2simi, gid2name, name_me)
print 'price adjustment...'; sys.stdout.flush()
price_adj(gid2simi, kvg)
print 'output...'; sys.stdout.flush()
output(sys.argv[5], gid2simi)
def main():
if len(sys.argv) != 4:
print "Usage: <goods.simi> <goods.docinfo> <output.html>"
sys.exit(0)
goods_simi = read_simi(sys.argv[1])
kvg = KVEngine()
kvg.load([full_path("goods_binfo.kv")])
kvg.load([full_path("goods_price.kv")])
kvg.load([full_path("goods_cat.kv")])
html = StringIO()
html.write('<head><meta http-equiv="Content-Type" content="text/html; charset=utf-8" /></head>')
case = 0
for gid in goods_simi:
title, imgurl, taobaourl, shopid, price, category = ginfo(kvg, gid)
html.write(main_snippet(case, gid, title, imgurl, taobaourl, shopid, price, category))
case += 1
items = goods_simi[gid].items()
items.sort(key=lambda x: x[1], reverse=True)
for subcase, item in enumerate(items):
rgid, weight = item
rtitle, rimgurl, rtaobaourl, shopid, price, category = ginfo(kvg, rgid)
html.write(sub_snippet(subcase, rgid, rtitle, rimgurl, rtaobaourl, weight, shopid, price, category))
subcase += 1
if subcase >= 10:
break
open(sys.argv[3], "w").write(html.getvalue())
def compute_cfss():
'''
计算shop-shop相似关系矩阵。
Input:
shop_actu：用户对店铺做的动作
Process:
取用户动作表示的shop向量，计算向量点积。
Output:
shop-shop 相似关系，cfss.kv
'''
# shop_actu -> shop-shop关系矩阵，并保存cfss.kv，shop\tshop:weight;
kvg = KVEngine()
kvg.load([full_path('shop_actu.kv')])
# get normialized vectors
shop_users = {}
skeys = kvg.keymatch('S\d+_ACTU')
for skey in skeys:
sid = key_id(skey)
vector = dict([(int(key), float(value)) for (key, value) in kvg.getd(skey).items() if key and value])
# tailor to top 20
items = vector.items()
items.sort(key=lambda x:x[1], reverse=True)
items = items[:20]
vector = dict(items)
normalize(vector)
shop_users[sid] = vector
# similarity calculation
shop_similarity = {}
sids = shop_users.keys()
sids.sort()
l = len(sids)
print "Calculating shop-shop similarity matrix, total %d..." % l
for i in range(l):
if i % 1000 == 0:
print "%d" % i
sys.stdout.flush()
for j in range(i+1, l):
sim = norm_dot_product(shop_users[sids[i]], shop_users[sids[j]])
if abs(sim) < 1e-5:
continue
shop_similarity.setdefault(sids[i], {})[sids[j]] = sim
shop_similarity.setdefault(sids[j], {})[sids[i]] = sim
# save as kvfile
write_kv_dict(shop_similarity, 'S%s_CFSIMS', 'cfss.kv')
def compute_cfgg():
'''
计算goods-goods相似关系矩阵。
Input:
user_actg.kv -> goods_actu.kv：用户对店铺做的动作
Process:
取用户动作表示的goods向量，计算向量点积。
Output:
goods-goods 相似关系，cfss.kv
'''
kvg = KVEngine()
kvg.load([full_path('goods_actu.kv')])
# get normialized vectors
goods_users = {}
gkeys = kvg.keymatch('G\d+_ACTU')
for gkey in gkeys:
gid = key_id(gkey)
vector = dict([(int(key), float(value)) for (key, value) in kvg.getd(gkey).items() if key and value])
# tailor to top 20
items = vector.items()
items.sort(key=lambda x:x[1], reverse=True)
items = items[:20]
vector = dict(items)
normalize(vector)
goods_users[gid] = vector
# similarity calculation
goods_similarity = {}
gids = goods_users.keys()
gids.sort()
l = len(gids)
print "Calculating goods-goods similarity matrix, total %d..." % l
for i in range(l):
if i % 100 == 0:
print "%d" % i
sys.stdout.flush()
for j in range(i+1, l):
sim = norm_dot_product(goods_users[gids[i]], goods_users[gids[j]])
if abs(sim) < 1e-5:
continue
goods_similarity.setdefault(gids[i], {})[gids[j]] = sim
goods_similarity.setdefault(gids[j], {})[gids[i]] = sim
# save as kvfile
write_kv_dict(goods_similarity, 'G%s_CFSIMG', 'cfgg.kv')
def main():
if len(sys.argv) != 6:
print 'Usage: <goods.simi> <goods.docinfo> <goods.name> <name.me> <goods.simi.output>'
sys.exit(0)
print 'docinfo...'
sys.stdout.flush()
gidx2id = read_docinfo(sys.argv[2])
print 'simi...'
sys.stdout.flush()
gid2simi = read_simi(sys.argv[1], gidx2id)
print 'name...'
sys.stdout.flush()
gid2name = read_name(sys.argv[3])
print 'name.me...'
sys.stdout.flush()
name_me = read_me(sys.argv[4])
kvg = KVEngine()
print 'binfo...'
sys.stdout.flush()
kvg.load([full_path('goods_binfo.kv')])
kvg.load([full_path('goods_price.kv')])
print 'shop limit...'
sys.stdout.flush()
same_shop_limit(gid2simi, kvg)
print 'mutually_exclusive_names...'
sys.stdout.flush()
mutually_exclusive_names(gid2simi, gid2name, name_me)
print 'price adjustment...'
sys.stdout.flush()
price_adj(gid2simi, kvg)
print 'output...'
sys.stdout.flush()
output(sys.argv[5], gid2simi)
def main():
kvg = KVEngine()
kvg.load([full_path('cfgg.kv')])
kvg.load([full_path('goods_binfo.kv')])
keys = kvg.keymatch('G\d+_CFSIMG')
sample_keys = random.sample(keys, 50)
html = StringIO()
html.write(
'<head><meta http-equiv="Content-Type" content="text/html; charset=utf-8" /></head>'
)
for key in sample_keys:
similar_goods = int_float_dict(kvg.getd(key))
items = similar_goods.items()
items.sort(reverse=True, key=lambda x: x[1])
items = [item for item in items if ('G%s-BINFO' % item[0]) in kvg][:10]
html.write(item_snippet(kvg, key, items))
open('a.html', 'w').write(html.getvalue())
def main():
kvg = KVEngine()
kvg.load([full_path('cfgg.kv')])
kvg.load([full_path('goods_binfo.kv')])
keys = kvg.keymatch('G\d+_CFSIMG')
sample_keys = random.sample(keys, 50)
html = StringIO()
html.write('<head><meta http-equiv="Content-Type" content="text/html; charset=utf-8" /></head>')
for key in sample_keys:
similar_goods = int_float_dict(kvg.getd(key))
items = similar_goods.items()
items.sort(reverse=True, key=lambda x:x[1])
items = [item for item in items if ('G%s-BINFO' % item[0]) in kvg][:10]
html.write(item_snippet(kvg, key, items))
open('a.html', 'w').write(html.getvalue())
def compute_cfss():
'''
计算shop-shop相似关系矩阵。
Input:
shop_actu：用户对店铺做的动作
Process:
取用户动作表示的shop向量，计算向量点积。
Output:
shop-shop 相似关系，cfss.kv
'''
# shop_actu -> shop-shop关系矩阵，并保存cfss.kv，shop\tshop:weight;
kvg = KVEngine()
kvg.load([full_path('shop_actu.kv')])
# get normialized vectors
shop_users = {}
skeys = kvg.keymatch('S\d+_ACTU')
for skey in skeys:
sid = key_id(skey)
vector = dict([(int(key), float(value))
for (key, value) in kvg.getd(skey).items()
if key and value])
# tailor to top 20
items = vector.items()
items.sort(key=lambda x: x[1], reverse=True)
items = items[:20]
vector = dict(items)
normalize(vector)
shop_users[sid] = vector
# similarity calculation
shop_similarity = {}
sids = shop_users.keys()
sids.sort()
l = len(sids)
print "Calculating shop-shop similarity matrix, total %d..." % l
for i in range(l):
if i % 1000 == 0:
print "%d" % i
sys.stdout.flush()
for j in range(i + 1, l):
sim = norm_dot_product(shop_users[sids[i]], shop_users[sids[j]])
if abs(sim) < 1e-5:
continue
shop_similarity.setdefault(sids[i], {})[sids[j]] = sim
shop_similarity.setdefault(sids[j], {})[sids[i]] = sim
# save as kvfile
write_kv_dict(shop_similarity, 'S%s_CFSIMS', 'cfss.kv')
def compute_cfgg():
'''
计算goods-goods相似关系矩阵。
Input:
user_actg.kv -> goods_actu.kv：用户对店铺做的动作
Process:
取用户动作表示的goods向量，计算向量点积。
Output:
goods-goods 相似关系，cfss.kv
'''
kvg = KVEngine()
kvg.load([full_path('goods_actu.kv')])
# get normialized vectors
goods_users = {}
gkeys = kvg.keymatch('G\d+_ACTU')
for gkey in gkeys:
gid = key_id(gkey)
vector = dict([(int(key), float(value))
for (key, value) in kvg.getd(gkey).items()
if key and value])
# tailor to top 20
items = vector.items()
items.sort(key=lambda x: x[1], reverse=True)
items = items[:20]
vector = dict(items)
normalize(vector)
goods_users[gid] = vector
# similarity calculation
goods_similarity = {}
gids = goods_users.keys()
gids.sort()
l = len(gids)
print "Calculating goods-goods similarity matrix, total %d..." % l
for i in range(l):
if i % 100 == 0:
print "%d" % i
sys.stdout.flush()
for j in range(i + 1, l):
sim = norm_dot_product(goods_users[gids[i]], goods_users[gids[j]])
if abs(sim) < 1e-5:
continue
goods_similarity.setdefault(gids[i], {})[gids[j]] = sim
goods_similarity.setdefault(gids[j], {})[gids[i]] = sim
# save as kvfile
write_kv_dict(goods_similarity, 'G%s_CFSIMG', 'cfgg.kv')
def main():
if len(sys.argv) != 4:
print 'Usage: <goods.simi> <goods.docinfo> <output.html>'
sys.exit(0)
goods_simi = read_simi(sys.argv[1])
kvg = KVEngine()
kvg.load([full_path('goods_binfo.kv')])
kvg.load([full_path('goods_price.kv')])
kvg.load([full_path('goods_cat.kv')])
html = StringIO()
html.write(
'<head><meta http-equiv="Content-Type" content="text/html; charset=utf-8" /></head>'
)
case = 0
for gid in goods_simi:
title, imgurl, taobaourl, shopid, price, category = ginfo(kvg, gid)
html.write(
main_snippet(case, gid, title, imgurl, taobaourl, shopid, price,
category))
case += 1
items = goods_simi[gid].items()
items.sort(key=lambda x: x[1], reverse=True)
for subcase, item in enumerate(items):
rgid, weight = item
rtitle, rimgurl, rtaobaourl, shopid, price, category = ginfo(
kvg, rgid)
html.write(
sub_snippet(subcase, rgid, rtitle, rimgurl, rtaobaourl, weight,
shopid, price, category))
subcase += 1
if subcase >= 10:
break
open(sys.argv[3], 'w').write(html.getvalue())
def compute_cfus():
'''
计算给用户推荐的店铺列表。
Input:
cfss: 店铺关系
user_favu: 用户关注店铺
user_actu: 用户有动作店铺
Process:
从用户直接相关店铺出发，找这些店铺的相关店铺，再过滤。
Output:
存储CF算法产生的给用户推荐的店铺列表。cfus.kv
'''
kvg = KVEngine()
kvg.load([full_path('cfss.kv')])
kvg.load([full_path('user_favs.kv')])
kvg.load([full_path('user_actu.kv')])
kvg.load([full_path('shop_binfo.kv')])
# get shop_similarity
keys = kvg.keymatch('S\d+_CFSIMS')
shop_similarity = dict([(int(key),
dict([(int(k), float(v))
for (k, v) in kvg.getd(key).items()]))
for key in keys])
# get user_fav_shops
keys = kvg.keymatch('U\d+_FAVS')
user_fav_shops = dict([(int(key), set([int(k) for k in kvg.getl(key)]))
for key in keys])
# get blocked shop set
keys = kvg.keymatch('S\d+_BINFO')
blocked_shops = set()
for key in keys:
if kvg.getk(key, 'block') != '0':
blocked_shops.add(key_id(key))
# get user tags by fav shops
shop_tags
# get user_shops
# shop idf
# weigting and normalizing user_shops
# 给每个用户做推荐
print "Recommend for each user, total %d" % len(self.user_shops)
sys.stdout.flush()
for no, uid in enumerate(self.user_shops):
shop_weight = {} # 给该用户推荐的店铺列表及权重
shops = self.user_shops[uid] # 用户有动作的店铺列表
fav_shops = self.user_fav_shops.get(uid, {}) # 用户关注的店铺
if no % 1000 == 0:
print "%d" % no
sys.stdout.flush()
for sid in shops:
if sid not in self.shop_similarity:
continue
simi_shops = self.shop_similarity[sid]
for ssid in simi_shops:
if ssid in shop_weight:
shop_weight[ssid] += shops[sid] * simi_shops[ssid]
else:
shop_weight[ssid] = shops[sid] * simi_shops[ssid]
# 过滤shop_weight
shop_weight_new = {}
for sid in shop_weight:
# 店铺sid是否适合推荐给用户uid
if sid in fav_shops:
continue # 原本就关注
if sid in self.shop_info and self.shop_info[sid][2] != 0:
continue # 店铺的block属性非0，被屏蔽，不使用
if sid in self.shop_tags and uid in self.user_tags and \
self._tag_conflict(self.user_tags[uid], self.shop_tags[sid]):
continue # 用户关注店铺的类型与该店铺不符
shop_weight_new[sid] = shop_weight[sid]
if not shop_weight_new:
continue # 没有为此用户推荐一个店铺，都被过滤掉，不记录
# 排序，取TOP
normalize(shop_weight_new)
items = shop_weight_new.items()
items.sort(reverse=True, key=lambda x: x[1]) # sort by weight desc
self.user_recommend_list[uid] = items[:TOP_SHOP_NUM] # limit n
def compute_cfus():
'''
计算给用户推荐的店铺列表。
Input:
cfss: 店铺关系
user_favu: 用户关注店铺
user_actu: 用户有动作店铺
Process:
从用户直接相关店铺出发，找这些店铺的相关店铺，再过滤。
Output:
存储CF算法产生的给用户推荐的店铺列表。cfus.kv
'''
kvg = KVEngine()
kvg.load([full_path('cfss.kv')])
kvg.load([full_path('user_favs.kv')])
kvg.load([full_path('user_actu.kv')])
kvg.load([full_path('shop_binfo.kv')])
# get shop_similarity
keys = kvg.keymatch('S\d+_CFSIMS')
shop_similarity = dict([(int(key), dict([(int(k), float(v)) for (k, v) in kvg.getd(key).items()])) for key in keys])
# get user_fav_shops
keys = kvg.keymatch('U\d+_FAVS')
user_fav_shops = dict([(int(key), set([int(k) for k in kvg.getl(key)])) for key in keys])
# get blocked shop set
keys = kvg.keymatch('S\d+_BINFO')
blocked_shops = set()
for key in keys:
if kvg.getk(key, 'block') != '0':
blocked_shops.add(key_id(key))
# get user tags by fav shops
shop_tags
# get user_shops
# shop idf
# weigting and normalizing user_shops
# 给每个用户做推荐
print "Recommend for each user, total %d" % len(self.user_shops)
sys.stdout.flush()
for no, uid in enumerate(self.user_shops):
shop_weight = {} # 给该用户推荐的店铺列表及权重
shops = self.user_shops[uid] # 用户有动作的店铺列表
fav_shops = self.user_fav_shops.get(uid, {}) # 用户关注的店铺
if no % 1000 == 0:
print "%d" % no
sys.stdout.flush()
for sid in shops:
if sid not in self.shop_similarity:
continue
simi_shops = self.shop_similarity[sid]
for ssid in simi_shops:
if ssid in shop_weight:
shop_weight[ssid] += shops[sid] * simi_shops[ssid]
else:
shop_weight[ssid] = shops[sid] * simi_shops[ssid]
# 过滤shop_weight
shop_weight_new = {}
for sid in shop_weight:
# 店铺sid是否适合推荐给用户uid
if sid in fav_shops:
continue # 原本就关注
if sid in self.shop_info and self.shop_info[sid][2] != 0:
continue # 店铺的block属性非0，被屏蔽，不使用
if sid in self.shop_tags and uid in self.user_tags and \
self._tag_conflict(self.user_tags[uid], self.shop_tags[sid]):
continue # 用户关注店铺的类型与该店铺不符
shop_weight_new[sid] = shop_weight[sid]
if not shop_weight_new:
continue # 没有为此用户推荐一个店铺，都被过滤掉，不记录
# 排序，取TOP
normalize(shop_weight_new)
items = shop_weight_new.items()
items.sort(reverse=True, key=lambda x: x[1]) # sort by weight desc
self.user_recommend_list[uid] = items[:TOP_SHOP_NUM] # limit n
